airflow README

The project's README from github.com/apache/airflow. As one markdown file: README.md.

Apache Airflow

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Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.

When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.

Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on Dags a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

Table of contents

Project Focus

Airflow works best with workflows that are mostly static and slowly changing. When the Dag structure is similar from one run to the next, it clarifies the unit of work and continuity. Other similar projects include Luigi, Oozie and Azkaban.

Beyond traditional data pipelines, Airflow is widely used to orchestrate machine learning workflows — training, retraining, evaluation, and deployment — and increasingly to orchestrate agentic and LLM-based workloads, coordinating the steps of an AI pipeline (data prep, tool calls, model invocation, evaluation) rather than acting as the agent itself. This isn't a new direction: teams have run ML and AI workloads on Airflow for years, and the ecosystem of providers supporting these use cases (see the AI & ML section of the Airflow registry) continues to grow.

Airflow is commonly used to process data, but has the opinion that tasks should ideally be idempotent (i.e., results of the task will be the same, and will not create duplicated data in a destination system), and should not pass large quantities of data from one task to the next (though tasks can pass metadata using Airflow's XCom feature). For high-volume, data-intensive tasks, a best practice is to delegate to external services specializing in that type of work.

Airflow is not a streaming solution, but it is often used to process real-time data, pulling data off streams in batches.

Principles

Requirements

Apache Airflow is tested with:

Main version (dev) Stable version (3.3.0) Deprecate version (2.11.2)
Python 3.10, 3.11, 3.12, 3.13, 3.14 3.10, 3.11, 3.12, 3.13, 3.14 3.10, 3.11, 3.12
Platform AMD64/ARM64 AMD64/ARM64 AMD64/ARM64(*)
Kubernetes 1.30, 1.31, 1.32, 1.33, 1.34, 1.35 1.30, 1.31, 1.32, 1.33, 1.34, 1.35 1.26, 1.27, 1.28, 1.29, 1.30
PostgreSQL 14, 15, 16, 17, 18 14, 15, 16, 17, 18 12, 13, 14, 15, 16
MySQL 8.0, 8.4, Innovation 8.0, 8.4, Innovation 8.0, Innovation
SQLite 3.15.0+ 3.15.0+ 3.15.0+

* Experimental

Note: MariaDB is not tested/recommended.

Note: SQLite is used in Airflow tests. Do not use it in production. We recommend using the latest stable version of SQLite for local development.

Note: Airflow currently can be run on POSIX-compliant Operating Systems. For development, it is regularly tested on fairly modern Linux Distros and recent versions of macOS. On Windows you can run it via WSL2 (Windows Subsystem for Linux 2) or via Linux Containers. The work to add Windows support is tracked via #10388, but it is not a high priority. You should only use Linux-based distros as "Production" execution environment as this is the only environment that is supported. The only distro that is used in our CI tests and that is used in the Community managed DockerHub image is Debian Bookworm.

Getting started

Visit the official Airflow website documentation (latest stable release) for help with installing Airflow, getting started, or walking through a more complete tutorial.

Note: If you're looking for documentation for the main branch (latest development branch): you can find it on s.apache.org/airflow-docs.

For more information on Airflow Improvement Proposals (AIPs), visit the Airflow Wiki.

Documentation for dependent projects like provider distributions, Docker image, Helm Chart, you'll find it in the documentation index.

Installing from PyPI

We publish Apache Airflow as apache-airflow package in PyPI. Installing it however might be sometimes tricky because Airflow is a bit of both a library and application. Libraries usually keep their dependencies open, and applications usually pin them, but we should do neither and both simultaneously. We decided to keep our dependencies as open as possible (in pyproject.toml) so users can install different versions of libraries if needed. This means that pip install apache-airflow will not work from time to time or will produce unusable Airflow installation.

To have repeatable installation, however, we keep a set of "known-to-be-working" constraint files in the orphan constraints-main and constraints-2-0 branches. We keep those "known-to-be-working" constraints files separately per major/minor Python version. You can use them as constraint files when installing Airflow from PyPI. Note that you have to specify correct Airflow tag/version/branch and Python versions in the URL.

  1. Installing just Airflow:

Note: Only pip and uv installation is currently officially supported.

While it is possible to install Airflow with tools like Poetry or pip-tools, they do not share the same workflow as pip - especially when it comes to constraint vs. requirements management. Installing via Poetry or pip-tools is not currently supported.

If you wish to install Airflow using those tools, you should use the constraint files and convert them to the appropriate format and workflow that your tool requires.

pip install 'apache-airflow==3.3.0' \
 --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-3.3.0/constraints-3.10.txt"
  1. Installing with extras (i.e., postgres, google)
pip install 'apache-airflow[postgres,google]==3.3.0' \
 --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-3.3.0/constraints-3.10.txt"

For information on installing provider distributions, check providers.

Installation

For comprehensive instructions on setting up your local development environment and installing Apache Airflow, please refer to the INSTALLING.md file.

Official source code

Apache Airflow is an Apache Software Foundation (ASF) project, and our official source code releases:

Following the ASF rules, the source packages released must be sufficient for a user to build and test the release provided they have access to the appropriate platform and tools.

Convenience packages

There are other ways of installing and using Airflow. Those are "convenience" methods - they are not "official releases" as stated by the ASF Release Policy, but they can be used by the users who do not want to build the software themselves.

Those are - in the order of most common ways people install Airflow:

All those artifacts are not official releases, but they are prepared using officially released sources. Some of those artifacts are "development" or "pre-release" ones, and they are clearly marked as such following the ASF Policy.

User Interface

Semantic versioning

As of Airflow 2.0.0, we support a strict SemVer approach for all packages released.

There are few specific rules that we agreed to that define details of versioning of the different packages:

airflow (apache/airflow)

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47.2K ·GitHub
Airflow
What is this?Apache Airflow is a platform to programmatically author, schedule, and monitor workflows, defined as code using Python.
Is this a library or a standalone application?It's both: Airflow is distributed as a Python package (apache-airflow on PyPI) but behaves like a full application with a scheduler, workers, database, and web UI that you deploy and run.
How do I get it running?Install it from PyPI with pip (or uv) using the official constraint files for a repeatable install, or run it via the community Docker images / Helm Chart on Kubernetes; the INSTALLING.md file covers full local setup.
What do I need before installing it — specific Python, database, OS?You need Python 3.10–3.14, a supported database (PostgreSQL 14-18, MySQL 8.0/8.4, or SQLite for local testing only), and a POSIX-compliant OS — officially Linux in production, with macOS fine for development.
Any install or platform pitfalls I should know about?Plain pip install apache-airflow can produce a broken install because dependencies are kept open rather than pinned, so you must use Airflow's constraint files; also Windows isn't natively supported — you need WSL2 or containers, and SQLite must never be used in production.
Who is this built for — what kind of workflows fit best?It's aimed at data/ML engineers running mostly static, slowly-changing pipelines; it's also increasingly used to orchestrate ML training/retraining and agentic/LLM pipeline steps, but it's not meant for streaming or passing large data volumes between tasks.
How do I visualize and debug a running pipeline?The web UI provides Dags, Grid, and Graph views to see run status over time and per-run dependency graphs, plus a Home page with environment summary stats and a Backfill view for rerunning historical date ranges.
Can tasks pass data between each other?Tasks are meant to be idempotent and shouldn't pass large data volumes directly; Airflow's XCom feature lets tasks exchange small amounts of metadata instead, with heavy data processing delegated to external systems.
How does Airflow compare to Luigi or Oozie?Airflow is listed alongside Luigi, Oozie, and Azkaban as a similar workflow orchestration tool, distinguished by its code-defined Dags, rich CLI, and web UI for monitoring.
What release channels are available besides PyPI?Besides pip-installable PyPI releases, Airflow offers convenience Docker images (for Kubernetes, Helm, docker-compose, docker swarm) and tagged GitHub source, though only the official ASF source packages are formal releases.
What are similar projects?dagster, argo-workflows, kestra, dolphinscheduler, airbyte, full-stack-data-engineering, taipy, prefect
Which topics is it in?data-integration, workflow-orchestration, data-pipelines, dag, etl, workflow-engine, apache, mlops, scheduler, data-engineering, workflow, orchestration
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CommitOct 17Release3.3.2Sep 17
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Commits · 90 Days